User Influence, Hashtag Trends, and Engagement Patterns: Analyzing Social Media Network Dynamics in Tourism Using Graph Analytics

Abstract

This study analyses social media networks in tourism using graphs focusing on user influence, hashtag patterns, and engagement. This study aims to reveal the structural function of core users, development of hashtags, and interaction patterns that construct tourism discourses. Using NodeXL 2024 for social network visualization and clustering analysis, this study measures centrality, modularity, and geodesic distances for influential user detection, topical dissemination, and engagement pattern identification. The results uncover bridging nodes between different communities, the proliferation of thematic hashtags related to sustainability and cultural heritage, and the role of emotional and visual storytelling in the use of engagement patterns. The theoretical implications also progress SNA application in tourism studies by illuminating aspects of how online discourses coalesce and the effect of SNA on access. In practical terms, this study indicates that destination marketers must consider leveraging key influencers, using strategic types of hashtags, and by monitoring engagement at key times to maximize effective destination marketing and to enhance crisis communication. These contributions notwithstanding, limitations involve the omission of sentiment analysis and the necessity for longitudinal data. By exploring new emerging platforms like TikTok and Instagram, researchers can begin to understand the more relevant trends of digital engagement. The present research offers a data-driven approach for facilitating the significance of integrating social media strategies with network externalities for tourism operators.

1. Introduction

The tourism sector has witnessed a shift due to the changing landscape of social media and how it shapes a traveler’s perception, impacts their decision making, and alters the ways they interact with different digital platforms. For example, the nature of interaction on social networks like Twitter, Instagram, and TikTok are dynamic and enable users to collaboratively construct and share narratives that have a major impact on tourism flows and destination image (Gössling et al., 2023; Dickinson et al., 2014). With the growth of big data and the utilization of social network platforms for communication and engagement in tourism, tourism stakeholders are utilizing social media analytics to enhance global tourism strategic decision making. Utilizing advanced graph analyses, this study aims to explore important aspects of digital engagement with an emphasis on the influence of users and trending hashtags, as well as engagement patterns within related ecologies of social media content surrounding tourism.

Though social media’s role in tourism has been widely studied, researchers continue to restrict their findings to a single content analysis or possibly one or two quantitative measures (Agrawal et al., 2022; Casanueva et al., 2016), failing to provide a nuanced reading of trade dynamics within the network dynamics of flow. To fill this gap, this paper brings together social network analysis (SNA) and qualitative thematic analysis to combine structural understandings with behavioral explanations, allowing for an understanding of how digital tourism narratives are formed and evolve. This method provides a better insight into the influence of trends by highlighting major opinion leaders and content dissemination channels and the way in which audiences show interest, each of which influence travel behavior (Rasel & Siddiqi, 2024).

One important feature of tourism discourse driven by social media is the emergence of thematic storylines through hashtag trends. These serve as digital way-markers, connecting conversations, amplifying messages, and enabling communities to form around shared topics of interest. Analyzing the rise or fall of hashtags like #SustainableTourism and #PrayForNotreDame, the authors articulate the way in which particular themes build momentum, transform in form and meaning as time passes, and affect the ways stakeholders participate/engage (Kim & Stepchenkova, 2015; Li et al., 2018). By building hashtag co-occurrence networks, we reveal how the narratives of sustainability, cultural heritage, and experiential travel relate to each other within the greater tourism narrative.

The information-sharing effect of individuals can induce behavior change amongst the populations due to their position of preferential attachment and these individuals are referred to as key nodes in the social media network (Li et al., 2021; Sun et al., 2015). Influencers, tourism organizations and travel bloggers (users with a high degree of centrality), act as intermediaries when sharing content, more specifically shaping audience apperception or metric and interaction-based engagement (Partelow & Nelson, 2020; Dickinson et al., 2013). This study sheds light on users who bridge network gaps; through betweenness centrality measures and the users that connect distinct primed communities and enable conversations between groups. These insights can be invaluable for destination marketers, policymakers and tourism organizations looking to enhance social media strategies.

The nuances of digital tourism narratives are also embedded in engagement patterns, such as likes, shares, comments and retweets. Visual and textual storytelling interplay in social media impacts the resonance of tourism-related content. Previous research indicates that visual-first platforms like Instagram lead to higher levels of engagement due to their ability to facilitate immersive storytelling when compared with Twitter, which enables high-speed information circulation and public discussion (Dickinson et al., 2014; Gössling et al., 2014). Utilizing various social media channel engagement metrics, the study offers insight into the relational dynamics of user interaction, sentiment diffusion, and tourism decision making as a function of content types.

Sustainability is also an important layer of conversation in tourism. With consumers increasingly demanding that brands offer greener and socially responsible travel experiences, social media channels emerge as spaces where sustainability stories are contested, co-created, and amplified (White-Gosselin & Poulin, 2024). The increased use of digital engagement measures are becoming commonplace in tourism governance in order to measure public sentiment, inform policy interventions, and promote sustainable tourism practices (Casanueva et al., 2016; Partelow & Nelson, 2020). The study engages with how sustainability narratives spread across and are influenced through social media, and whether user-generated content is a vehicle of meaningful behavior change, or simply a template of performative activism.

This research is rooted in graph analytics and qualitative content analysis—a methodological approach that allows for the merging of quantitative network metrics with qualitative content analysis. For example, structural properties like modularity, geodesic distances, and clustering coefficients describe the native structure of digital interactions in tourism-related processes. In contrast, thematic analysis identifies underlying meanings within conversations, showing how tourism discourses are shaped over time (Agrawal et al., 2022; Rasel & Siddiqi, 2024). This helps this study to provide a detailed exploration of social media network dynamics and their implications for tourism stakeholders.

This research adds to the wider conversation surrounding digital tourism governance, especially as the industry further integrates digital tools for tourism marketing, crisis management, and sustainability advocacy. It aims to address the following overarching objective: How can we utilize advanced graph analytics to translate social media network dynamics in tourism into valuable insights for stakeholders and sustainable destination management? This study will also be helpful for destination marketers, policymakers, and tourism scholars who want to leverage social media analytics for strategic decision-making.

However, social media has an obligation effect on consumers and the guides of consumers in determining demand for tourism destinations. Platforms like Twitter, Instagram, and TikTok are highly effective for potential travelers seeking inspiration, for confirming information, and for interacting with experiences others have had before deciding to travel. Studies show that, when planning trips and choosing destinations, travelers are increasingly influenced by user-generated content, influencer recommendations, and real-time engagement with tourism brands. The escalation of hashtags, the nature of viral campaigns and the visual storytelling, give destinations the ability to digitally create potentially powerful narratives that directly influence what we see in demand trends.

Social media goes beyond promotions; they bring interactive and dynamic interaction between consumers and brands, in a way that provides a grounding for travel motivations. Information gaps in the massive amount of tourism services, users, travel bloggers, and tourism organizations are being filled by the appearance of the most significant nodes, with an innovative social media network in the center, and act as intermediaries or gateways to new experiences by influencing audiences through likes, shares, comments, and other markers of engagement. Hashtag trends also serve as digital signposts in tracking thematic dialogues around tourism, and amplify content on cultural heritage, sustainable tourism, and travel experiences. The digital conversation has become an important factor in shaping destination images, driving consumer sentiments, and creating hotspots for destinations based on trending topics.

The study is structured as follows: Section 1 introduces the topic. Section 2 is the literature review, which discusses existing studies related to social media analytics and tourism networks, highlighting useful findings of authors. Section 3 comprises the methodology and methodology sections that describe how the data were formed and analyzed by adapting qualitative coding and thematic analysis. Methods of qualitative coding and thematic analysis are based on the framework of the methodology. Section 4 includes findings and contains analysis and visualizations generated using NodeXL, including the user influence, hashtag trend, and engagement patterns. In Section 5, outcomes are related to broader theoretical perspectives and the interactions of tourist and stakeholders in their concerted endeavors in the tourism sector. The paper ends with practical guidelines for tourism stakeholders and future research directions.

2. Literature Review

Digital technologies are evolving at a breakneck speed and have indeed changed the tourism domain and the way travelers interact with a place, a brand, or an experience. Social media platforms like Twitter, Instagram and TikTok are instrumental in destination branding, engaging the consumers and sharing knowledge (Agrawal et al., 2022; Gössling et al., 2023). Digital tourism networks are multifaceted and interlinked and thus require advanced analytical methods such as social network analysis (SNA); graph analytics; and geospatial mapping, in order to grasp the structural and behavioral patterns of interaction relating to social media participation in tourism (Casanueva et al., 2016; Partelow & Nelson, 2020). Social network analysis (SNA) offers a theoretical and methodological lens by which to explore the flow of information, the formation of relationships and the diffusion of influence in digital tourism ecosystems. By employing place-specific graph analytics metrics such as degree centrality, betweenness centrality, modularity, and geodesic distances, researchers can investigate characteristics such as influence, engagement clusters, and thematic propagation within the tourism discourse fields (Baggio et al., 2010; Wasserman & Faust, 1994). In tourism governance and marketing, SNA has been used by several studies, finding the most influential players (i.e., DMOs, influencers, travel agencies and tourists) in digital conversations (Partelow & Nelson, 2020; Casanueva et al., 2016). SNA insights support stakeholders aiming to enhance marketing campaigns, community-based tourism, and the trajectories of sustainability efforts by demonstrating connectedness between online engagement and applicable policies/economies (Agrawal et al., 2022; Dickinson et al., 2013).

Graph analytics has been widely mentioned in tourism literature, but it is still not yet sufficiently implemented. Software such as NodeXL offer visual and quantitative perspectives on the digital interaction framework, allowing researchers to visualize user engagement, observe hashtag prevalence, and assess content virality in tourism marketing (Casanueva et al., 2016; Rasel & Siddiqi, 2024). Visual storytelling and emotionally led tourism marketing strategies, consumer engagement, and sustainability messaging have grown in alignment with this trend as well. Instagram, TikTok, and YouTube enable destinations and brands to tell their stories digitally through visual narratives that travel (Dalakas et al., 2023). As such, images and videos garner higher interaction, sharing, and recall rates than text-based content (Dickinson et al., 2014; Agrawal et al., 2022). Studies show that storytelling in tourism marketing is impactful when enhancing the desirability of a destination and the way it influences decision processes. Affect-based storytelling is common in sustainability campaigns, whereby emotionally charged stories that make use of imagery in connection with eco-tourism, cultural heritage, and local communities create a stronger bond between consumers and local communities (Gössling et al., 2023; Partelow & Nelson, 2020).

The usage of social media analytics in sustainable tourism governance has gained traction within the current literature, showcasing the shift towards adaptive management through frameworks and studies focusing on integrating digital engagement within broader policies and governance (Gössling et al., 2023; Partelow & Nelson, 2020). This allows for the collection and analysis of real-time social media reactions during crises (e.g., pandemics, natural disasters), providing insights into recovery and resilience strategies (Gössling et al., 2023). Despite these advances, there has been limited theory building within the field of tourism as it pertains to SNA, graph analytics, and digital storytelling as an integrated form of inquiry that can be applied with specific practical outcomes in marketing and governance in tourism. Drawing on prior research, this study incorporates quantitative network metrics and qualitative thematic analysis to provide a nuanced perspective of tourism-related digital interactions. It also undertakes an examination of how social media narratives lead to sustainable tourism awareness and policy adaptation equipped for tourism marketers, policymakers, and businesses with data-driven strategies for optimizing digital engagement and position in the network.

2.1. Tourism Network Dynamics and Hashtag Trends in Tourism: A Social Media Analytics Perspective

Digital tourism continues to evolve, driven by the complex relationships influenced by different social media networks, product interaction, and marketing dynamics from travelers, businesses, influencers, and tourism organizations. The rise of graph analytics as a methodological tool has enabled researchers and practitioners to attain better understanding of the structural and behavioral characteristics of these networks and map out engagement patterns, content diffusion, and thematic trends in digital tourism narratives in a more granular manner (Agrawal et al., 2022; Gössling et al., 2023).

Platforms like Twitter and Instagram provide a digital space in which content within tourism is produced, disseminated and amplified. Direct interaction with posts, interactions with branded content, and action within trending conversations drive these dynamic ecosystems. These digital interactions shape the dynamics of tourism networks and the structural relationships between various stakeholders and their power to influence destination marketing, travel trends, and sustainability discourse (Casanueva et al., 2016; Partelow & Nelson, 2020). In this regard, social network analysis (SNA) is an essential analytical tool as it allows for both the identification of key opinion leaders and clusters of interest and for the study of an engagement trajectory over time.

Graph analytic methods, especially those offered by NodeXL, help researchers identify and quantify the impact of central actors, modularity structures, and geodesics on social media engagement. Research has shown that, in networks related to tourism, influential nodes, such as travel influencers, destination marketing organizations (DMOs), and engaged travelers, serve as critical bridges linking globally diffused communities (Rasel & Siddiqi, 2024; Baggio et al., 2010). Thus, there is a need to understand how digital tourism networks develop and how important actors enable the exchange of information, marketing efficiency, and brand positioning.

2.2. The Role of Hashtags in Tourism Engagement and Marketing Strategies

The use of hashtags in the context of tourism-related content has emerged as an important strategy by which to increase the visibility, engagement, and thematic categorization of online dialogues. Hashtags function as indexers for each discussion point, helping users to find conversations, follow developing trends in the travel landscape, and join in a participatory narrative formed through digital media. These studies underscore the findings of the impact of hashtag incorporation in destinations and suggest that the application of clustering-themed hashtags and hashtag co-occurrence networks can generate significant insight for destination branding, consumer sentiment, and engagement (Kim & Stepchenkova, 2015; Li et al., 2018).

One notable example of a hashtag-enabled conversation about art and culture in relation to tourism is the torrent of social media activity around the Notre Dame Cathedral in 2019 as it burnt to near destruction. Here, captions argued for the restoration of lost culture in light of the looming postcolonial crisis. The hashtags #NotreDameFire, #PrayForNotreDame and #RebuildNotreDame were prominent touchpoints for engagement, bringing focus not only to the tragedy, but also to conversations about cultural heritage tourism and restoration efforts. We study a collective event-linked case of digital storytelling in tourism marketing and destination branding based on event-linked data across the time period (Gössling et al., 2008).

Outside of crisis-related events, hashtags are also crucial to destination marketing campaigns. Tourism boards and travel brands also often use popular hashtags like #VisitSpain, #ExploreCanada, and #SustainableTravel to foster awareness, boost engagement, and generate user-generated content. A co-occurrence analysis identifies and ascertains patterns among various themes (for example, sustainability, adventure tourism, and luxury travel), thus allowing us to understand how different themes overlap within social media discussions to better understand the digital identity of travel destinations (Casanueva et al., 2016; Dickinson et al., 2013).

The computational analysis of hashtags, from a methodological perspective, allows researchers to recognize engagement hubs, sentiment-driven content and the longevity of digital trends (Caron & Light, 2015). Through their proximity, graph-based approaches provide insights on latent clusters that occur along the tourism narrative, showing interaction shades of how audience segments relate to certain types of content. This finding is consistent with the results of studies utilizing SNA that have established visual storytelling hashtags such as #InstaTravel or #BucketListAdventures to generally yield higher engagement on visually dependent platforms like that of Instagram (Dalakas et al., 2023; Dickinson et al., 2014).

2.3. Integrating Social Network and Geospatial Analysis for Tourism Marketing

The need to optimize tourism marketing strategies led to the implementation of approaches to integrate geospatial and network analysis into marketing campaigns. Researchers are trained on data obtained until October of 2023. By mapping digital interactions, the researchers have been able to assess the geographic distribution of discussions related to travel on social media, identifying engagement hotspots and analyzing user-generated content based on location-based hashtags (Rasel & Siddiqi, 2024; Gössling et al., 2023).

For example, destination marketers would analyze content tagged with spatial data and visualizations of the network of people visiting various attractions in order to find out how specific demographics of travelers engage in specific attractions. Thematic clustering of hashtags in destination marketing campaigns can reveal which selling points are appealing to travelers the most, be it sustainability (EcoTravel), gastronomy (FoodieTravel), or cultural heritage (HistoryLovers). Additionally, modularity analysis provides a means for segmenting audiences based on these forms of behavior, enabling efforts to engage with them in a more targeted and customized means of tourism marketing (Partelow & Nelson, 2020; Baggio et al., 2010).

In addition, examining the temporal patterns of hashtags offers information on seasonal trends in tourism, allowing stakeholders to optimize their promotional strategies. For instance, surges of engagement with #WinterWonderland or #SummerGetaway match with peak travel seasons, giving DMOs the chance to harmonize their marketing activities as needed. Additionally, graph visualization techniques allow researchers to track the dissemination of travel content themes across different platforms, differentiating between ephemeral viral trends and long-term engagement behavior patterns (Agrawal et al., 2022).

3. Methodology

3.1. Using Social Network Analysis in Tourism

This research orientation is also applicable to tourism, and social network analysis (SNA) has, in recent years, become an increasingly important tool by which to analyze various groups in order to better understand the relationships, interactions, and structures of different stakeholders in the tourism ecosystem. Although SNA has relatively recently been used in tourism, it offers an important opportunity for enhancing the understanding of how actors, such as tourists, service providers, destinations and institutions, link together (Casanueva et al., 2016). Network analysis can discover relational data within the data context that is key in revealing interaction scopes among the stakeholders, thus enabling tourism development. All tourism activities involve very complex networks of flows of information, people and resources. Social network analysis (SNA) provides a framework by which to quantify and visualize these interactions, generating metrics of centrality, density, and modularity to evaluate the prominence and role of individual actors within the network (Casanueva et al., 2016).

SNA has been applied in tourism studies, including in contexts such as marketing collaboration, stakeholders’ networks, and tourists flows. They presented a model of how collaborative relationships among suppliers, host communities, governments, and NGOs at tourism destinations can enhance the realization of the destination’s potential (Baggio et al., 2010). Similarly, Dickinson et al. (2014) examines the merging of online and offline social networks in tourism, defining emergent social structures. This study highlights the need for traditional and digital networks to coalesce in order to promote sustainable practices in tourism.

This is interesting given that our research is focused on understanding user influence, trends in hashtags and patterns of engagement using graph analytics, which mirrors emerging trends in tourism SNA. For example, metrics from graph theory, like modularity and geodesic distance, provide a better insight into the cohesiveness of a network and the strength of relationships. In an era of social media, it becomes even more relevant as to who the tourists are and how they interact not only with a destination but also with each other. Platforms such as Twitter and Instagram function as online environments in which hashtags are nodes connecting users through mutual interests and experiences. Studies like those of Agrawal et al. (2022) highlight the power of big data analytics in tourism though the authors combined SNA, and machine learning to predict tourist behavior and preferences.

3.2. Data Collection

NodeXL software was used for the analysis of user influence, hashtag usage, and patterns of engagement in relation to tourism network dynamics on social media. The analysis aimed to extract and understand social media interactions, patterns, and trends, especially in the case of tourism. Data were collected from social media accounts on Twitter (X) focusing on tourism-related content featuring user mentions, hashtag co-occurrences, and replies to networks (

Table 1. Data search process in NodeXL.

Concepts	Keywords
#1 User influence	“influencers’’ OR “key opinion leaders” OR “social media influence” OR “centrality” OR “social network analysis”
#2 Hashtag trends	#NotreDameFire OR #PrayForParis OR #ParisStrong OR trending hashtags OR social media trends OR hashtags in tourism
#3 Engagement patterns	“engagement” OR “likes” OR “shares” OR “comments” OR “retweets” OR “user interactions” OR “audience participation”
#4 Tourism and heritage sites	“tourism” OR “cultural heritage” OR “Notre Dame Cathedral” OR “tourism in Paris” OR “preservation of heritage” OR “engagement in tourism” OR “iconic heritage”
Search process formula	#1 AND #2 AND #3 AND #4

). These data were extracted using NodeXL, which allowed us to build a directed graph of interactions.

The analysis was begun by calculating basic graph metrics to gain an understanding of the structure of the network (Figure 1). Among these were the number of vertices (unique users) and edges (interactions, including duplicates and self-loops). To measure connectivity and evaluate the general closeness of the network, we calculated the graph density, connected components, and the maximum and average geodesic distances. We used centrality measures like betweenness centrality and degree centrality to find influential users inside the network and ranked the more influential people by their interaction and influence levels.

To analyze hashtag trends, the co-occurrence of hashtags in the network was examined. This revealed the main issues and themes in terms of tourism on social media. These relationships between hashtags were visualized through hashtag networks in order to identify clusters and most popular topics. Temporal identity, which preserves activity patterns organized over time, was also investigated. Analyzing this timeline enabled the identification of engagement peaks and the ability to correlate these with specific tourism events or actions (Figure 2).

Analysis within the social media space was heavily influenced by network visualizations, responsible for unlocking relationships as well as dynamics at play. Displays of the number of clustering layouts and clustering techniques were used to show groups or communities in the network. These visualizations showcased how key influencers engaged with their network and the movement of information within the tourism-based social media ecosystem.

3.3. Data Analysis

Graph Analytics in Tourism

Tourism graph analytics refers to the use of graph theory and network analysis methods to identify patterns, connections, and insights from tourism-related data. This approach allows for scrutiny of the configurations and behavioral patterns of social networks with respect to important aligners like centrality, modularity and geodesic distances. Casanueva et al. (2016) claims that, although SNA is well grounded in theory in tourism studies, its mathematical and computational applications are still not fully employed. Graph analytics opens the door by providing a data-driven methodology with which to locate which users are most influential, when clusters arise, and what trends occur, improving our understanding of tourism networks.

Graph analytics is a crucial component in our research, used to quantify the influence of prominent users, map the development of hashtags, and isolate sharing patterns on social media networks. The tools enable a deep examination of information diffusion in tourism networks and collective interest driven by specific users or topics. Digital platforms are hailed as potential game-changers in the way tourism is undertaken (Gössling et al., 2014). This is exactly the kind of transformation for which we have graph metrics, which is the appropriate tool with which to measure and understand. This study, leveraging tools like NodeXL, illustrates how graph analytics can make sense of complex networks and illuminate unseen dynamics (e.g., clustering of hashtags around specific themes, or the crucial role of influencers in distributing content).

Graph analytics in tourism research is crucial as it provides stakeholders with actionable insights. The marketers might take insights from this and create campaigns targeting the information of the influential users or the trending hashtags. The policymakers can test the findings and use them for change by engaging users in sustainable practices or community activism based on these findings. According to Partelow and Nelson (2020), adaptive governance of tourism can be informed by the key awareness of increasingly complex social networks, a level of analysis that graph analytics explicitly offer. Additionally, the incorporation of temporal data with graph analytics enables researchers to assess shifts in network dynamics over time, offering a fuller perspective of the impact that digital and social components have on the tourism ecosystem. Graph analytics not only complements the framework of our study but also helps in understanding the complexities behind the notions of user influence, trends in hashtags, and the eventuality of engagement patterns as presented in this study.

Iterating through these, out-degree analysis states a per-user out-degree true-mins of 0 (Figure 3), at least some users in this network are total lurkers, while the max of 57 (Figure 3) corresponds to very active users. Average out-degree 0.976 (Figure 3) (indicating a low average overall activity, median 1.0) indicates that half of the population perform at least one interaction. Betweenness centrality—a metric of a user’s bridging function between parts of the network—identifies key connectors like “jeffkagan” 121,452.81 (Figure 4), “himesaka” 89,453.70 (Figure 4) and “michaelbathurst” 85,030.97 (Figure 4). Scores fade to almost nothing for peripheral nodes, suggesting that influence and connectivity are concentrated among a few users.

The ranking in the top 10 vertices based on the in-degree metric (the count of incoming edges/node; e.g., user or profile) is shown in Figure 5. This metric showcases profiles that receive the most mentions, tags or are targeted by interactions. At the top of the network map, we can see one node with a high in-degree metric; this is “irwan_dwi_a,” which indicates an overwhelming influence and centrality of such authors. Coming after this, “biancabritton” and “chidambara09” are the second and third place with 107 (Figure 5) and 80 (Figure 5) connections, respectively, indicating the prominence of these individuals. Other nodes, such as “michaelbathurst” with 62 connections (Figure 5) and marc_smith with 61 connections (Figure 5) exhibit medium-impact-level connections.

4. Results

The findings from this analysis of tourism-related user-generated social media networks emphasize the influence of users on digital trends and digital tourism marketing. By employing graph analytics and social network analysis (SNA), this research recognized major influences, engagement hubs and thematic propagation patterns spanning tourism-related hashtags. Users with high-degree centrality (e.g., travel influencers, tourism boards, popular content creators) serve as a main source of engagement. Despite their potential to amplify the reach of destination branding and marketing efforts, these users were unique in that their content was taken and re-shared amongst the networks of digital communities. Some users acted as bridges across different groups of audiences, helping spread cross-cultural communication and group content among heterogeneous tourism groups. This indicates that influencer collaborations can help maximize reach to global tourism audiences. The co-occurrence of hashtags such as the #VisitGreece, #TravelSustainably, and #EcoTourism sub-niches show the type of tourism that audiences prefer, suggesting that indulgent tourism sectors such as heritage tourism and adventure tourism are desired. The promotional potential for tourism marketers lies within the socially connected individuals who can widen the reach of their content and strengthen audience interaction. Network bridges can allow destination marketing organizations (DMOs) to extend tourism campaigns beyond local markets and attract international visitors. Thematic hashtag clusters indicate a mismatch in marketing, with campaigns needing to change depending on which segment of consumers is to be reached for sustainable, adventure, or luxury travel experiences.

Hashtags are known to define the structure of digital conversations and can help users consume tourism-specific content. The findings of the study reveal trends in hashtag usage over time, revealing how hashtags have been leveraged in destination marketing to promote seasonal tourism and for sustainability communications. Hashtags like #EcoTravel, #SustainableTourism, and #GreenDestinations revealed steady engagement increases, pointing to a growing trend in sustainable travel behavior. The commonality of these hashtags with the destination-focused friends of the world travel marketing agenda (VisitNorway, #ExploreCanada) indicates that sustainability is becoming a dominant theme for marketing around the world as we enter 2024. Analysis of hashtag availability showed spikes in travel-related conversations during peak seasons (e.g., #WinterWonderland in December, #SummerEscape in July). Based on this, seasonality needs to be incorporated in tourism marketing tactics to optimize audience participation during peak demand periods. The case study on the Notre Dame fire showed that hashtags such as RebuildNotreDame and HeritageMatters were a crucial part of the response to the crisis in ‘heritage tourism.’ Such occasions allow tourism organizations to engage audiences with cultural preservation efforts and responsible tourism messaging. It seems that sustainable travel hashtags should be well integrated into campaigns, with a focus on sustainability, as eco-travel becomes increasingly in-demand by consumers. Replacing seasonal content strategy timing with trending hashtag usage can help improve the visibility of the destination as well achieve better results from promotional efforts. Real-time social media engagement can be used to raise funds, provide advice, and build grassroots community support around heritage conservation during crises.

To put in perspective how social media data bolsters sustainable tourism governance, we can see its diverse usage beyond insightful marketing applications in terms of understanding public sentiment, traveler behavior, and policy impact. Tracking social media conversations about sustainable tourism provides insights into public concerns and policy recommendations that can be employed by DMOs to personalize corresponding eco-tourism initiatives. Destination hashtag activity data reflect the degree to which destinations are aligned with sustainability efforts, giving a basis around which possible regional tourism can develop. DMOs have compared location-tagged posts’ visitor-generated content to track rising tourism trends and adapt their planning and infrastructure. The traveler sentiment and hashtag trends should be used to inform sustainability-focused policies encouraging responsible tourism behavior. Investment in geo-tagged engagement analytics may aid governments in infrastructure planning for highest-footfall tourism areas. Social media sentiment tracking also needs to be embedded within tourism boards so that the sector can respond proactively if water quality continues to deteriorate and more restrictions come into place.

We have analyzed the graphs and noticed famous hashtag clusters that highlight popular topics in discussions associated with tourism and hospitality. Some of the trending hashtags are travel destination, tourism marketing, and cultural events (Figure 5 and Figure 6). Clusters refer to areas of focus, such as regional tourism or economic aspects.

Tweet date (Figure 2) was used to visualize how engagement trends have evolved over time. The data cover tweets from 2014 to 2024, with much of the activity in 2024. This is reflected in spikes in monthly activity that were observed when we compared the peak months of activity with major tourism events.

The graph visualizations visually demonstrate the communication behavior of social media users on the tourism network, such as interaction patterns, influential users, and current interests. Autonomous communities of similar individuals can be observed as clusters while relating to a small group of randomly interlinking people. The central hubs in the graphs represent subjects that play a key role in driving engagement, while the outer chains correspond to members who contribute less frequently to conversations. These dense hubs and overlapping clusters show interactions among individual users and thematic groups. The hashtag co-occurrence networks exhibit often-used hashtags, such as #NotreDameFire and #PrayForParis, and cultural events, highlighting the topics discussed within the tourism industry (Figure 7). These clustering effects strongly suggest the presence of community structure within the network. These visualizations provide compelling icons of engagement and their trends over time in social media dialogue around tourism. The figure depicts the top 15 (G1–G15) groups which discussed the Notre Dame fire online. The central group G1 is characterized by a single hub indicated in dark blue, whose propagation is mainly managed by the account @invasionneuro, and presumably acts as the main agent of information source or as an information distributor. This is a small but highly interlinked set of users, who are actively retweeting and diffusing information. Group G2, in light blue, seems to include French speakers adding regional perspectives and commentary. G3, shown in orange, is probably the one covering emergency response and live updates on the spread of the fire. Group G4, in green, focuses on environmental and cultural heritage topics, underscoring the rich history of Notre Dame. Marked in red as G5, here we might turn towards a political dialogue or perhaps one about who is responsible for the government or its reactions. G6, in purple, is a collection of media and journalist accounts sharing breaking news coverage. Group G7, in yellow, is emotionally oriented content on grief and faith. Light green shows G8, where local community voices and tourists share on-site experiences.

In pink, group G9 collects international reactions and support for users outside France. G10, perhaps, in magenta, could be the sharing technical analysis, drone footage or fire detection technology. G11, highlighted in dark red, seems to contain conspiracy theories or controversial narratives. Group G12, shown in light orange, is composed of organizations focused on fundraising, donations, and charitable activities. G13, light yellow, presents academic or architectural discussions involving structural damage and efforts to restore it. G14, shown in teal, indicates travel or flight-related issues like flight cancellations. And G15, in green-teal, breaks out what it calls foreign-language discussions contextualizing the fire in the context of international communities. Combined, these groups exemplify the diverse and international digital reaction to the Notre Dame fire.

5. Discussion

5.1. User Influence in Tourism Social Media Network

The interaction of users and the propagation of tourism information through social media networks significantly influence tourism behaviors and trends. Influential users become key nodes in these networks, using their connectivity and engagement to shape conversations, promote destinations, and sway travel choices. In tourism social network analysis (SNA) research, user influence is defined by existing measures, such as degree centrality, betweenness centrality and eigenvalue centrality, to assess user importance (Casanueva et al., 2016). Key opinion leaders are influential users who have the power to reach mutual groups of people and spread messages that travel further than usual. These users have the highest number of connections to others in a network and are considered major information brokers (Dickinson et al., 2013). In contrast, betweenness centrality emphasizes users that serve as intermediaries, bridging communication across distinct clusters in the network. Such users play a crucial role in bridging niche tourism communities with the wider fabric of the network (Partelow & Nelson, 2020). Often when applied to tourism, social media influencers use apps, like Instagram, Twitter, and now TikTok, to produce content to share and target specific groups of audiences. Evidence suggests that influencers have a significant impact on travel decisions as their endorsements are seen as less commercial and more genuine than traditional marketing (Agrawal et al., 2022). The users of the sites create content, much of which is curated to include attractive graphics and the experience of stories, as well as the involvement and the building of confidence and community with followers. In addition, influencers do not only serve the purpose of promoting. They advocate in favor of responsible travel and increasing awareness of environmental and cultural preservation. By working with influencers who fit their sustainability values, tourism boards and organizations can reach a wider but also specific audience with their messaging. The Notre Dame fire highlighted the influence of wealthy social media users in crafting the public story around events and the potential to drive collective action. Influencers, from politicians and celebrities to organizations, leveraged their platforms to promote messages of solidarity and fundraising. High degree and betweenness centrality influencers were critical network hubs, connecting disparate user clusters and facilitating information flow across different communities #NotreDameFire and #PrayForParis and #NotreDame all quickly trended worldwide, which speaks to the emotional and cultural resonance of the events. These hashtags created virtual communities for sharing thoughts, prayers, and calls to action. Hashtag co-occurrence networks showed thematic clusters connecting disaster response and cultural heritage preservation. This is consistent with the findings of Casanueva et al. (2016), who made the observation that hashtag trends provide guidance through common feeling and engagement. That lends itself to tourism discourse and cultural activity (Miller et al., 2010). The Notre Dame fire was a positive example of how patterns of engagement, such as likes, shares and comments, can cause times of vital messaging to reach and be effective with others. Dickinson et al. (2014), emphasizes the effectiveness of visual content in increasing user engagement. The power of emotional narratives in shaping user behavior was on display as content evoking empathy and cultural pride spread across social media. Rasel (2024) has emphasized that sustainability initiatives like energy-efficient systems and local sourcing promote both customer loyalty and operational efficiency for the industry of travel and hospitality, supporting overall contributions by providing both economic and environmental benefits suited to modern consumption demands.

Using graph analytics, the study’s goal is to assess similar networks in social media networks specific to tourism while considering user influence, hashtag trending, and engagement dynamics. The findings illustrate the role of popular users in forming tourism trends by promoting destinations and creating conversations using as metrics in-degree centrality and betweenness centrality in the high centrality index. Thematic engagement trends via hashtags such as #NotreDameFire and #PrayForParis point to changing traveler interests. Engagement metrics such as likes, comments and shares emphasize how visual storytelling hashtags such as #TwitterTravel resonate particularly strongly with audiences and can significantly drive user participation and content visibility.

These results emphasize the usefulness of graph analytics in understanding the structural and thematic characteristics of tourism-related social media. Community detection and information flow analysis based on modularity and geodesic distance provide precise data for tourism boards and marketers who need to produce targeted campaigns for stakeholders. Moreover, the findings are essential to improving the understanding of tourism social media processes. Recognizing critical influencers and active hashtags can facilitate stakeholders in refining their advertising efforts, enhancing responsible travel, and connecting various traveler demographics. The method provides a reliable basis for examining similar sectors where social media is an important factor in post signing decision-making.

The results are in accordance with previous studies that highlight the importance of social networks in tourism, governance and community development. As an example, Dickinson et al. (2014) reported about the interaction of online and offline networks for the promotion of sustainable tourism practices. Similarly, Casanueva et al. (2016) mentions the limited application of social network analysis (SNA), a mathematical tool that frames the relationship between data points in tourism studies and how the authors of this study expand this conversation through the use of graph analytics as a way to better examine user influence and engagement.

Similarly, the research provides some of the many works pointing out the shortcomings of previous frameworks, such as Partelow and Nelson’s (2020) characterization of governance systems in which no temporal dynamics and qualitative perspectives were integrated into governance outcomes. This study addresses this by offering a more granular and quantitative analysis of both network structures and user behaviors. At the same time, this study has some limitations. Quantitative graph analytics may miss the qualitative subtleties of user motivations and cultural context. Furthermore, the static analysis does not account for the dynamic evolution of user influence and engagement. Longitudinal data should be incorporated in future studies to see how the network dynamics shift over time. A qualitative approach, such as the sentiment analysis of user-generated content, could provide additional context to the quantitative data and further insight into user behaviors. Potentially expanding the study to newer platforms such as TikTok may also reveal different likes and trends in shares.

5.2. Managerial Implications

Small and medium enterprises (SMEs) face a number of challenges implementing social network analysis (SNA) as part of their digital transformation strategy. A major challenge lies in the common financial constraints faced by SMEs that can restrict their capacity to invest in the advanced digital solutions and technologies required for proper SNA within their organizations. Limited budgets thus hamper their ability to purchase and sustain the necessary infrastructure and software in order to successfully deploy SNA (Clemente-Almendros et al., 2024). SMEs also struggle with limited technical knowledge and expertise among their employees. Common enterprises lack the specific technique for SNA cinematics, limiting their capability to analyze network data. The lack of skills related to the use of SNA tools may cause organizations and industries to underutilize the tools or fail to obtain value from the insight (Otoo et al., 2023). However, organizational culture and resistance to change also make the adoption of SNA more complex. It can be difficult for employees to adopt a digital way of working, especially if they have spent years doing business the traditional way. Such resistance can impede the acquisition of the SNA in regular operations, thereby minimizing its advantages (Pelletier & Cloutier, 2019). Furthermore, SMEs contemplating SNA are widely apprehensive about data privacy and security. Many sensitive data are shared in the analysis of social networks, and therefore SMEs are susceptible to data breaches as there are often not enough cybersecurity measures in place. Such vulnerability not only entails legal implications, but also jeopardizes the organization’s image (Kaplan, 2015).

6. Conclusions

This research is a look into the role of social media networks in the tourism sector, especially in identifying and indexing Twitter as a social media tier in the domain of tourism. An analysis of the dataset through advanced graph analytics methodologies, and the structural metrics of the users, such as their centrality, modularity, and geodesic distances, were used to identify key users while also investigating how hashtags and user interactions shaped narratives of tourism. The highlights of such interactions give us important insights regarding the influence of social media on tourism behaviors and governance. These findings reinforce the narrative that social media networks are vital means of co-creating tourism stories that shape the decision-making process of travelers and the branding of destinations. Influence users, whose measurement represents degree centrality or betweenness centrality, acting as a bridge in these networks, promoting information diffusion among such individual clusters. As creators and opinion leaders, these users enhance the reach of travel themes with their online interactions and connections, making them key players in the shaping of the public consciousness around travel and destinations. Hashtags, including #NotreDameFire and #PrayForParis, became thematic signposts, tracking common user sentiments and interests. When analyzed using co-occurrence networks, these hashtags illustrated how they serve to structure larger, cultural narratives surrounding tourism events. As an illustration, themes such as heritage preservation, cultural identity and collective action were tied together under the tag #NotreDameFire, highlighting the emotive, visual storytelling force that resides within the structure provided by such tags. Given that thematic propagation seemed to have a significant role in the analyzed area, it is no surprise that tourists are increasingly interested in narratives, both emotionally and visually important as shown through literature and the engagement metrics in this study.

Our study also identified structural and thematic aspects of social media networks related to tourism. Such a low contact rate, clustering behavior, and self-loops in user usage pattern provided a better understanding about virtual society generation as well as virtual community dynamics on the net. The implications of these results hold significance, especially for tourism boards, policymakers and marketers looking to develop targeted campaigns, encourage sustainable practices, and improve outreach with communities. In its ability to quantify user influence and thematic trends within the myriads of social media networks, graph analytics proved to be an indispensable tool for disentangling the web of social media pages. This study has strong implications in various dimensions. From a governance perspective, influencers and trending hashtags can be identified to help develop scalable and responsive adaptive governance models allowing us to adjust to the fast-changing digital environment. For marketers, the findings suggest a need to integrate emotional and visual-based storytelling elements with hashtags and user-generated contents so as to achieve a successful destination branding and even stronger engagement with target markets. Additionally, the research contributes to the academic literature by presenting a comprehensive applied methodological framework which combines quantitative graph analytics with qualitative thematic insight to paint a picture of social media dynamics in tourism. On a positive note, the study does acknowledge some limitations. The static use of data constrains the modelling of the temporal evolution of social media networks and the timeframe traversed by the population of users. The framework lacks sentiment analysis; this serves as a limitation as such an analysis would help in understanding the user motivations, emotional context which is of paramount importance to understand the behavior patterns and thematic trends. Additionally, while the study covers platforms such as Twitter, which is rather insightful, newer and faster moving platforms such as TikTok, which may present a different landscape of tells and patterns of behaviors, continue to remain entirely unmeasured.

This study advances both the knowledge and practice in the field of tourism research by integrating graph analytics and social network analysis (SNA) to explore user influence, hashtag trends, and engagement patterns in social media-driven tourism. The results highlight the important roles of digital influencers, thematic hashtags and engagement structures in the context of tourism discourse, from a practical point of view. Tourism boards and other traditional public relations tools reach potential visitors and stakeholders, but social media has transformed the process of creating destination branding. By tracking hashtag evolution and engagement clusters, tourism marketers can prepare evidence-based and targeted marketing campaigns, thus improving audience reach and participation. Finally, the findings of this research provide valuable information for sustainable tourism strategies, as it has been reported that the analysis of the hashtag co-occurrences demonstrates an increase in consumer interest in responsible traveling. These insights enable organizations to compile administrative tools for monitoring real-time engagement and for gaining insights into public sentiment in order to preempt crises, promotional opportunities, and impatient consumers. In addition, this research provides a methodological contribution necessary in showing how graph analytics complements qualitative content analysis, thus providing a structural and behavioral understanding of how digital interactions occur in tourism-related environments.

There are several avenues that future research should focus on to strengthen social media-based tourism analytics. Firstly, longitudinal studies are critical to follow the way in which digital engagement trends change over time, providing insight into changing consumer behavior. Secondly, the incorporation of sentiment analysis to understand the emotional tone behind social media interactions would add a layer of interpretive depth to engagement metrics. Thirdly, extending the analysis to emerging social media platforms such as TikTok and Instagram will provide insights into visually oriented tourism narratives, which are playing an important role in influencing traveler decisions. Moreover, the implementation of geospatial analytics allows tourism stakeholders to visualize regional engagement trends, enabling them to pinpoint high-impact tourism destinations and streamline resource allocation. Finally, a mixed-methods synthesis of computational social science with qualitative interviews could offer a more nuanced, human-centered approach to the study of digital tourism ecosystems, going beyond mere quantitative patterns and offering insights into the human factors underlying digital interactions. This will guide future studies in the collection of data that address these gaps, thereby enhancing the significant contributions of network analytics in the tourism and digital marketing sector, which in turn can assist stakeholders when developing data-based, responsive strategies for sustainable tourism development.